On Mon, Nov 4, 2019 at 4:54 PM PIERRE AUGIER < pierre.aug...@univ-grenoble-alpes.fr> wrote:
> Dear Python-Numpy community, > > Transonic is a pure Python package to easily accelerate modern > Python-Numpy code with different accelerators (currently Cython, Pythran > and Numba). > > I'm trying to get some funding for this project. The related work would > benefit in particular to Cython, Numba, Pythran and Xtensor. > > To obtain this funding, we really need some feedback from some people > knowing the subject of performance with Python-Numpy code. > > That's one of the reason why we wrote this long and serious text on > Transonic Vision: http://tiny.cc/transonic-vision. We describe some > issues (perf for numerical kernels, incompatible accelerators, community > split between experts and simple users, ...) and possible improvements. > Thanks Pierre, that's a very interesting vision paper. In case you haven't seen it, there was a discussion on the pandas-dev mailing list a couple of weeks ago about adopting Numba as a dependency (and issues with that). Your comment on my assessment from 1.5 years ago being a little unfair to Pythran may be true - not sure it was at the time, but Pythran seems to mature nicely. The ability to switch between just-in-time and ahead-of-time compilation is nice. One thing I noticed is that this actual switching is not completely fluent: the jit and boost decorators have different signatures, and there's no way to globally switch behavior (say with an env var, as for backend selection). > Help would be very much appreciated. > I'd be interested to help think about adoption and/or funding. Cheers, Ralf > > Now a coding riddle: > > import numpy as np > from transonic import jit > > @jit(native=True, xsimd=True) > def fxfy(ft, fn, theta): > sin_theta = np.sin(theta) > cos_theta = np.cos(theta) > fx = cos_theta * ft - sin_theta * fn > fy = sin_theta * ft + cos_theta * fn > return fx, fy > > @jit(native=True, xsimd=True) > def fxfy_loops(ft, fn, theta): > n0 = theta.size > fx = np.empty_like(ft) > fy = np.empty_like(fn) > for index in range(n0): > sin_theta = np.sin(theta[index]) > cos_theta = np.cos(theta[index]) > fx[index] = cos_theta * ft[index] - sin_theta * fn[index] > fy[index] = sin_theta * ft[index] + cos_theta * fn[index] > return fx, fy > > How can be compared the performances of these functions with pure Numpy, > Numba and Pythran ? > > You can find out the answer in our note http://tiny.cc/transonic-vision > :-) > > Pierre > > > Message: 1 > > Date: Thu, 31 Oct 2019 21:16:06 +0100 (CET) > > From: PIERRE AUGIER <pierre.aug...@univ-grenoble-alpes.fr> > > To: numpy-discussion@python.org > > Subject: [Numpy-discussion] Transonic Vision: unifying Python-Numpy > > accelerators > > Message-ID: > > < > 1080118635.5930814.1572552966711.javamail.zim...@univ-grenoble-alpes.fr> > > > > Content-Type: text/plain; charset=utf-8 > > > > Dear Python-Numpy community, > > > > Few years ago I started to use a lot Python and Numpy for science. I'd > like to > > thanks all people who contribute to this fantastic community. > > > > I used a lot Cython, Pythran and Numba and for the FluidDyn project, we > created > > Transonic, a pure Python package to easily accelerate modern > Python-Numpy code > > with different accelerators. We wrote a long and serious text to explain > why we > > think Transonic could have a positive impact on the scientific Python > > ecosystem. > > > > Here it is: http://tiny.cc/transonic-vision > > > > Feedback and discussions would be greatly appreciated! > > > > Pierre > > > > -- > > Pierre Augier - CR CNRS http://www.legi.grenoble-inp.fr > > LEGI (UMR 5519) Laboratoire des Ecoulements Geophysiques et Industriels > > BP53, 38041 Grenoble Cedex, France tel:+33.4.56.52.86.16 > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
